Online advertisement placements generally refer to the slots or space on the pages of a website that are available for displaying advertisements along with its content. Advertisers typically bid on these advertisement placements that are made available through real-time bidding (RTB) exchanges such as AdX, Admeld, Pubmatic, etc.
From a mechanical perspective, this requires the bidding server to have computer hardware linked to the exchanges. The bidding server then receives bid requests via the exchanges. A bid request occurs when a user/internet surfer visits a website/publisher that is selling their ad space on an RTB exchange. Upon receiving a bid request, the bidding server has a very short period of time within to respond to this request (generally around 50-100 ms or less).
Since this bid response needs to occur in a very short period of time, it is difficult to run large scale models to predict what ads to buy and what price to pay for them. We propose methods for creating optimal bid time decisions that allow multiple large data sets to be used in the decision processing in a way that can be acted upon in the time frame required for real-time bidding.
Traditionally, an advertiser manually made simple static rules to be carried out at bid time. The advertiser observed and determined which domains were available on the exchanges. The advertiser selected the domains to bid on by entering them into an excel document. Then, after several days, the advertiser received a report and visually weighs each domain against its click-through-rate (“CTR”) to decide if the advertisement performed adequately. The CTR refers to the percentage of times users click on the advertisements given the number of times the advertisements are displayed (“impressions”). The advertiser removed poor performing domains and added new domains. This traditional approach was largely a process of trial and error that relied to a great extent on human memory and human judgment in an effort to meet CTR goals and to ensure enough domains were chosen so that the campaign met the daily impression quota. The traditional approach is more prone to human errors. Furthermore, because all domains were generally bid on with a single static price, advertisers often paid too much for advertisement placements or did not win the more valuable bids.